| LMest: An R package for latent Markov models for longitudinal categorical data F Bartolucci, S Pandolfi, F Pennoni Journal of Statistical Software 81, 1-38, 2017 | 108 | 2017 |
| A comparison of some criteria for states selection in the latent Markov model for longitudinal data S Bacci, S Pandolfi, F Pennoni Advances in Data Analysis and Classification 8, 125-145, 2014 | 79 | 2014 |
| A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection S Pandolfi, F Bartolucci, PN Friel Journal of Machine Learning Research - Proceedings of International …, 2010 | 45 | 2010 |
| Three-step estimation of latent Markov models with covariates F Bartolucci, GE Montanari, S Pandolfi Computational Statistics & Data Analysis 83, 287-301, 2015 | 36 | 2015 |
| Dealing with reciprocity in dynamic stochastic block models F Bartolucci, MF Marino, S Pandolfi Computational Statistics & Data Analysis 123, 86-100, 2018 | 26 | 2018 |
| Dimensionality of the Latent Structure and Item Selection Via Latent Class Multidimensional IRT Models F Bartolucci, GE Montanari, S Pandolfi Psychometrika 77, 782-802, 2012 | 23 | 2012 |
| Discrete latent variable models F Bartolucci, S Pandolfi, F Pennoni Annual Review of Statistics and Its Application 9 (1), 425-452, 2022 | 22 | 2022 |
| A generalized multiple-try version of the reversible jump algorithm S Pandolfi, F Bartolucci, N Friel Computational Statistics & Data Analysis 72, 298-314, 2014 | 20 | 2014 |
| LMest: an R package for latent Markov models for categorical longitudinal data F Bartolucci, A Farcomeni, S Pandolfi, F Pennoni arXiv preprint arXiv:1501.04448, 2015 | 15 | 2015 |
| Latent ignorability and item selection for nursing home case-mix evaluation F Bartolucci, GE Montanari, S Pandolfi Journal of Classification 35, 172-193, 2018 | 12 | 2018 |
| An exact algorithm for time-dependent variational inference for the dynamic stochastic block model F Bartolucci, S Pandolfi Pattern Recognition Letters 138, 362-369, 2020 | 9 | 2020 |
| Evaluation of long-term health care services through a latent Markov model with covariates GE Montanari, S Pandolfi Statistical Methods & Applications 27, 151-173, 2018 | 9 | 2018 |
| Item selection by latent class-based methods: an application to nursing home evaluation F Bartolucci, GE Montanari, S Pandolfi Advances in Data Analysis and Classification 10, 245-262, 2016 | 9 | 2016 |
| A hidden Markov model for continuous longitudinal data with missing responses and dropout S Pandolfi, F Bartolucci, F Pennoni Biometrical Journal 65 (5), 2200016, 2023 | 7 | 2023 |
| LMest: Latent Markov Models with and without Covariates F Bartolucci, S Pandolfi R package version 2 (1), 2017 | 7 | 2017 |
| A comparison of some estimation methods for latent Markov models with covariates F Bartolucci, GE Montanari, S Pandolfi Proceedings of COMPSTAT, 531-538, 2014 | 7 | 2014 |
| A joint model for longitudinal and survival data based on an AR (1) latent process S Bacci, F Bartolucci, S Pandolfi Statistical Methods in Medical Research 27 (5), 1285-1311, 2018 | 6 | 2018 |
| A new constant memory recursion for hidden Markov models F Bartolucci, S Pandolfi Journal of Computational Biology 21 (2), 99-117, 2014 | 6 | 2014 |
| Hybrid maximum likelihood inference for stochastic block models MF Marino, S Pandolfi Computational Statistics & Data Analysis 171, 107449, 2022 | 5 | 2022 |
| Comment on the paper “On the memory complexity of the forward–backward algorithm,” by Khreich W., Granger E., Miri A., Sabourin, R. F Bartolucci, S Pandolfi Pattern Recognition Letters 38, 15-19, 2014 | 5 | 2014 |
Francesco BartolucciProfessor of Statistics - Department of Economics - University of PerugiaPreverjeni e-poštni naslov na stat.unipg.it
